Start with a short introduction about what is going on in this document
Introduction to PDCA, CAST and the methods contained within this document go here.
Explain some background info that is needed. Change header colours to purple?
Explanation of that equation and a definition using code..?
Step through explanation of 2 stream sample data included. Plot both oil and gas production. Pick one well to
#Display chart of production data using plotly. Oil first.
oil_prod<-plot_ly(data = formated_production$CD, x = ~CD.Month, y = ~Oil.Rate, color = ~Unique.Well.ID, type='scatter',mode='lines')
oil_prod<-layout(oil_prod, xaxis = list(title = "Cal-Day Month"),yaxis = list(title = "Oil Rate (bbls/d)"), title = "Example Oil Production")
#Then gas
gas_prod<-plot_ly(data = formated_production$CD, x = ~CD.Month, y = ~Gas.Rate, color = ~Unique.Well.ID, type='scatter',mode='lines')
gas_prod<-layout(gas_prod, xaxis = list(title = "Cal-Day Month"),yaxis = list(title = "Gas Rate (Mcfd)"), title = "Example Gas Production")
combined<-subplot(oil_prod,gas_prod,nrows = 2,shareX = FALSE,titleX = FALSE,titleY = TRUE)
combined<-layout(combined,showlegend=FALSE,title="Oil & Gas Production",xaxis2=list(title="Cal-Day Month"))
combinedExplain the background of what it is and how it can be helpful in forecasting, especially to handle uncertainty. Define PDCA here! Explain briefly bayesian methods of achieving this? Priors and posteriors
Explain the place of the monte carlo method as our first try at estimating the posterior ditribution, showing parameter distributions as defining our prior and explaining how they are each sampled
Explain here and have links to jump around document? Or explain when we get to it?
Explain process of forecast sythesis here, and show how it is carried out for the modified hyperbolic. Show arps substitution, and explain
How do we compare the synthesis forecasts against our actuals and determine what is a good fit and what is a poor fit? S
Define and apply soft L1 loss and show plot of a forecast against actuals colour by individual cost
Explain this loss function, where it’s from, and show a comparison of application to the Soft L1 loss
Show evaluation of the loss function to come up with average cost parameters for each forecast Show plots coloured by avg cost
Show best fit using either loss function and explain how this may be a good fit for wells with good production history, we still want to create a probabilistic outcome for each well to capture forecast uncertainty
Show top 10% of functions, etc. using either loss function, and show that the Monte Carlo method returns adequate forecasts for good prod history, but not short history wells.. What’s next?? MCMC
Explain concept or link above if put there.
Show MC method as burn-in to determine best fit forecast to apply PDCA loss function to
Creation of P10, P50, Avg, and P90 forecasts
Darcy Redpath
Petronas Canada
dredpath@petronascanada.com